Letian Wang

Hi there! I'm a Ph.D. student at the University of Toronto, where I am so fortunate to be supervised by the brilliant Prof. Steven Waslander (Nicest Steve Ever!) in the Toronto Robotics and AI Lab. I'm affiliated with Vector Institute. Previously, I was also fortunate to do research with/at:

  • Prof. Changliu Liu, at CMU Robotics Institute (2020-2022)
  • Prof. Masayoshi Tomizuka, at UC Berkeley (2019-2020)
  • Prof. Fu Zhang, at HKUST Robotics Institute (2018)
  • and as a Research Intern at/with
  • NVIDIA Research, with Prof. Marco Pavone (2023-2024)
  • SenseTime Research, with Yu Liu (2021)
  • I obtained my bachelor's degree with highest honor at Beihang University, China.

    My research interests lie in the intersection between autonomous driving, robotics, machine learning, computer vision, with special interest in 3D vision, multimodal agent, end-to-end driving, human-robot interaction, and behavior forecasting. I recently focus on developping generalizable decision-making and scalable perception systems, powered by foundation models and learning paradigm that scales well with data.

    I have authored 1 book, was the winner of 2022 CARLA autonomous driving challenge, and won the best paper award honorable mention at RA-L 2021, first prize in the National Challenge Cup 2017 (全国挑战杯一等奖, known as the Olympics of sci./tech. for university students in China), and co-founded a start-up in industrial UAVs.


    G. Scholar     LinkedIn     Twitter

    I am open to collaborations, feel free to reach out!

    letianwang0 at gmail dot com

    Some recent highlights from our research:
    2022.09 - Present
    UofT

    PhD student at University of Toronto, supervised by Steven Waslander. We're interested in perception and decision-making algorithms that can adapt to open-world settings.

    2023.09 - 2024.06
    NVIDIA

    Research intern at the Autonomous Vehicle Group of NVIDIA Research, with Peter Karkus, Seung Wook Kim, Boris Ivanovic, Yue Wang, Sanja Fidler, and Marco Pavone. I worked on self-supervised representation learning via generalizable neural radiance field, toward exploring potential foundation model for autonomous driving [NeurIPS'24].

    2021.05 - 2023.02
    SenseTime Research

    Research intern at X-Lab of SenseTime Research, with Yu Liu. I worked on efficient reinforcement learning via temporal abstraction and prior knowledge exploitation [RSS'23, IROS'23], and safe end-to-end driving [CVPR'23, CORL'22] (Winner of CARLA Autonomous Driving Challenge)

    2020.10 - 2022.05
    Carnegie Mellon University

    Research assistant at the ICL Lab, with Changliu Liu and Yeping Hu, at the Robotics Institute of Carnegie Mellon University. I worked on generalizable motion prediction algorithms in different scenarios, and social interaction for autonomous driving [NeurIPS'21, AAAI'22, Book]

    2019.10 - 2020.10
    UC Berkeley

    Research assistant at the MSC Lab, with Liting Sun, Wei Zhan, and Masayoshi Tomizuka, at UC Berkeley. I worked on socially-compatible behavior generation for autonomous driving [RA-L'21] (Best Paper Award Honorable Mention)

    2018.07 - 2018.09
    Hong Kong University of Science and Technology

    Research assistant at the HKUST RI, with Fu Zhang. I worked on unified control strategy for vertically take-off and landing UAVs.

    * denotes equal contribution

    DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features

    Letian Wang, Seung Wook Kim, Jiawei Yang, Cunjun Yu, Boris Ivanovic, Steven L Waslander, Yue Wang, Sanja Fidler, Marco Pavone, Peter Karkus
    Advances in Neural Information Processing Systems (NeurIPS 2024)
    Webpage  •   PDF  •   Video

    Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning

    Hao Shao, Shengju Qian, Han Xiao, Guanglu Song, Zhuofan Zong, Letian Wang, Yu Liu, Hongsheng Li
    Advances in Neural Information Processing Systems (NeurIPS 2024, Spotlight)
    PDF  •  

    SmartRefine: An Scenario-Adaptive Refinement Framework for Efficient Motion Prediction

    Yang Zhou, Hao Shao, Letian Wang, Steven L Waslander, Hongsheng Li, Yu Liu
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024)
    Outperform all published ensemble-free works on Argoverse 2 leaderboard (single agent track).
    PDF  •   Code

    LmDrive: Closed-Loop End-to-End Driving with Large Language Models

    Hao Shao, Yuxuan Hu, Letian Wang, Steven L Waslander, Yu Liu, Hongsheng Li
    IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024)
    First work to bring LLM into closed-loop end-to-end autonomous driving.
    Webpage  •   PDF  •   Code

    Accelerating Reinforcement Learning for Autonomous Driving using Task-Agnostic and Ego-Centric Motion Skills

    Tong Zhou*, Letian Wang*, Ruobing Chen, Wenshuo Wang, Yu Liu
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
    PDF  •  

    Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors

    Letian Wang, Jie Liu, Hao Shao, Wenshuo Wang, Ruobing Chen, Yu Liu, Steven L Waslander
    Robotics: Science and Systems (RSS 2023)
    PDF  •   Code

    ReasonNet: End-to-End Driving with Temporal and Global Reasoning

    Hao Shao, Letian Wang, Ruobing Chen, Steven L Waslander, Hongsheng Li, Yu Liu
    Conference on Computer Vision and Pattern Recognition (CVPR 2023)
    Winner of CARLA Autonomous Driving Challenge 2022
    PDF  •   Code

    Social Interactions for Autonomous Driving: A Review and Perspectives

    Wenshuo Wang, Letian Wang, Chengyuan Zhang, Changliu Liu, Lijun Sun
    Foundation and Trends in Robotics (Book)
    PDF  •  

    Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer

    Hao Shao*, Letian Wang*, Ruobing Chen, Hongsheng Li, Yu Liu
    Conference on Robot Learning 2022
    First Place on the CARLA Leaderboard (Sensor Track)
    PDF  •   Code

    Efficient Game-Theoretic Planning with Prediction Heuristic for Socially-Compliant Autonomous Driving

    Chenran Li, Tu Trinh, Letian Wang, Changliu Liu, Masayoshi Tomizuka, Wei Zhan
    IEEE Robotics and Automation Letters 2021
    PDF  •  

    Human Instruction Following: Graph Neural Network Guided Object Navigation

    Hongyi Chen, Letian Wang, Yuhang Yao, Ye Zhao, Patricio Vela
    The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022, Workshop on Embodied AI
    PDF  •  

    Transferable and Adaptable Driving Behavior Prediction

    Letian Wang, Yeping Hu, Liting Sun, Wei Zhan, Masayoshi Tomizuka, Changliu Liu
    arxiv
    PDF  •  

    Online Adaptation of Neural Network Models by Modified Extended Kalman Filter for Customizable and Transferable Driving Behavior Prediction

    Letian Wang, Yeping Hu, Changliu Liu
    AAAI Conference on Artificial Intelligence, Workshop on Human-Centric Self-Supervised Learning
    PDF  •  

    Hierarchical Adaptable and Transferable Networks (HATN) for Driving Behavior Prediction

    Letian Wang, Yeping Hu, Liting Sun, Wei Zhan, Masayoshi Tomizuka, Changliu Liu
    Conference on Neural Information Processing Systems (NeurIPS 2021), Workshop on Machine Learning for Autonomous Driving (Spotlight)
    PDF  •  

    Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data

    Letian Wang, Liting Sun, Masayoshi Tomizuka, Wei Zhan
    IEEE Robotics and Automation Letters 2021 Best Paper Award - Honorable Mention
    PDF  •  

    Overall Design and Control of Coaxial Tilt Rotor Vertically Take-off-and-Landing UAV

    Letian Wang, Yuhan Lu, Yibo Liu, Yicong Fu, Bonan Xu, Jingyu Zhao, Qi Qian, Yifan Yan, Weijun Wang
    First prize in National Challenge Cup 2017 (全国挑战杯一等奖, known as the Sci./Tech. Olympics among universities in China).
    Starting point for our UAV start-up journey for the later 2 years

    Some of my slides can be found here

    2024

    Toronto TechTalk

    2021

    NeurIPS Workshop on Machine Learning for Autonomous Driving

    2020

    INFORMS Annual Meeting

    2020+

    Reviewer: IJRR, RSS, NeurIPS, CVPR, ICRA, IROS, ML4AD, TNNLS, TVT, ITS, IV